Overview

Dataset statistics

Number of variables28
Number of observations9994
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.7 MiB
Average record size in memory915.4 B

Variable types

Numeric11
Text6
DateTime2
Categorical9

Alerts

Country has constant value "United States"Constant
Category is highly overall correlated with Sub-CategoryHigh correlation
Cost is highly overall correlated with Sales and 1 other fieldsHigh correlation
Days_to_ship is highly overall correlated with Ship ModeHigh correlation
Discount is highly overall correlated with Profit and 2 other fieldsHigh correlation
Postal Code is highly overall correlated with Region and 1 other fieldsHigh correlation
Profit is highly overall correlated with Discount and 2 other fieldsHigh correlation
Region is highly overall correlated with Postal Code and 1 other fieldsHigh correlation
Sales is highly overall correlated with Cost and 2 other fieldsHigh correlation
Ship Mode is highly overall correlated with Days_to_shipHigh correlation
State is highly overall correlated with Postal Code and 2 other fieldsHigh correlation
Sub-Category is highly overall correlated with CategoryHigh correlation
is_gain is highly overall correlated with Discount and 2 other fieldsHigh correlation
profit_margin is highly overall correlated with Discount and 2 other fieldsHigh correlation
sales_per_quantity is highly overall correlated with Cost and 1 other fieldsHigh correlation
Row ID is uniformly distributedUniform
Row ID has unique valuesUnique
Discount has 4798 (48.0%) zerosZeros
Days_to_ship has 519 (5.2%) zerosZeros

Reproduction

Analysis started2025-11-23 23:58:29.066208
Analysis finished2025-11-23 23:58:47.421597
Duration18.36 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Row ID
Real number (ℝ)

Uniform  Unique 

Distinct9994
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4997.5
Minimum1
Maximum9994
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-11-23T23:58:47.504410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile500.65
Q12499.25
median4997.5
Q37495.75
95-th percentile9494.35
Maximum9994
Range9993
Interquartile range (IQR)4996.5

Descriptive statistics

Standard deviation2885.1636
Coefficient of variation (CV)0.57732139
Kurtosis-1.2
Mean4997.5
Median Absolute Deviation (MAD)2498.5
Skewness0
Sum49945015
Variance8324169.2
MonotonicityStrictly increasing
2025-11-23T23:58:47.616118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99941
 
< 0.1%
11
 
< 0.1%
21
 
< 0.1%
31
 
< 0.1%
41
 
< 0.1%
51
 
< 0.1%
61
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
Other values (9984)9984
99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
99941
< 0.1%
99931
< 0.1%
99921
< 0.1%
99911
< 0.1%
99901
< 0.1%
99891
< 0.1%
99881
< 0.1%
99871
< 0.1%
99861
< 0.1%
99851
< 0.1%
Distinct5009
Distinct (%)50.1%
Missing0
Missing (%)0.0%
Memory size615.0 KiB
2025-11-23T23:58:47.818350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters139916
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2538 ?
Unique (%)25.4%

Sample

1st rowCA-2016-152156
2nd rowCA-2016-152156
3rd rowCA-2016-138688
4th rowUS-2015-108966
5th rowUS-2015-108966
ValueCountFrequency (%)
ca-2017-10011114
 
0.1%
ca-2017-15798712
 
0.1%
us-2016-10850411
 
0.1%
ca-2016-16533011
 
0.1%
us-2015-12697710
 
0.1%
ca-2016-10573210
 
0.1%
ca-2015-13133810
 
0.1%
ca-2015-1584219
 
0.1%
ca-2016-1451779
 
0.1%
ca-2015-1326269
 
0.1%
Other values (4999)9889
98.9%
2025-11-23T23:58:48.063414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
125510
18.2%
-19988
14.3%
015492
11.1%
215381
11.0%
C8308
 
5.9%
A8308
 
5.9%
67904
 
5.6%
77438
 
5.3%
47400
 
5.3%
57338
 
5.2%
Other values (5)16849
12.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)139916
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
125510
18.2%
-19988
14.3%
015492
11.1%
215381
11.0%
C8308
 
5.9%
A8308
 
5.9%
67904
 
5.6%
77438
 
5.3%
47400
 
5.3%
57338
 
5.2%
Other values (5)16849
12.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)139916
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
125510
18.2%
-19988
14.3%
015492
11.1%
215381
11.0%
C8308
 
5.9%
A8308
 
5.9%
67904
 
5.6%
77438
 
5.3%
47400
 
5.3%
57338
 
5.2%
Other values (5)16849
12.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)139916
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
125510
18.2%
-19988
14.3%
015492
11.1%
215381
11.0%
C8308
 
5.9%
A8308
 
5.9%
67904
 
5.6%
77438
 
5.3%
47400
 
5.3%
57338
 
5.2%
Other values (5)16849
12.0%
Distinct1237
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
Minimum2014-01-03 00:00:00
Maximum2017-12-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-23T23:58:48.163036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:48.283787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1334
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
Minimum2014-01-07 00:00:00
Maximum2018-01-05 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-23T23:58:48.384308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:48.501254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Ship Mode
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size603.5 KiB
Standard Class
5968 
Second Class
1945 
First Class
1538 
Same Day
 
543

Length

Max length14
Median length14
Mean length12.823094
Min length8

Characters and Unicode

Total characters128154
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSecond Class
2nd rowSecond Class
3rd rowSecond Class
4th rowStandard Class
5th rowStandard Class

Common Values

ValueCountFrequency (%)
Standard Class5968
59.7%
Second Class1945
 
19.5%
First Class1538
 
15.4%
Same Day543
 
5.4%

Length

2025-11-23T23:58:48.628427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T23:58:48.708390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
class9451
47.3%
standard5968
29.9%
second1945
 
9.7%
first1538
 
7.7%
same543
 
2.7%
day543
 
2.7%

Most occurring characters

ValueCountFrequency (%)
a22473
17.5%
s20440
15.9%
d13881
10.8%
9994
7.8%
C9451
7.4%
l9451
7.4%
S8456
 
6.6%
n7913
 
6.2%
t7506
 
5.9%
r7506
 
5.9%
Other values (8)11083
8.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)128154
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a22473
17.5%
s20440
15.9%
d13881
10.8%
9994
7.8%
C9451
7.4%
l9451
7.4%
S8456
 
6.6%
n7913
 
6.2%
t7506
 
5.9%
r7506
 
5.9%
Other values (8)11083
8.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)128154
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a22473
17.5%
s20440
15.9%
d13881
10.8%
9994
7.8%
C9451
7.4%
l9451
7.4%
S8456
 
6.6%
n7913
 
6.2%
t7506
 
5.9%
r7506
 
5.9%
Other values (8)11083
8.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)128154
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a22473
17.5%
s20440
15.9%
d13881
10.8%
9994
7.8%
C9451
7.4%
l9451
7.4%
S8456
 
6.6%
n7913
 
6.2%
t7506
 
5.9%
r7506
 
5.9%
Other values (8)11083
8.6%
Distinct793
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size556.4 KiB
2025-11-23T23:58:48.928676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters79952
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowCG-12520
2nd rowCG-12520
3rd rowDV-13045
4th rowSO-20335
5th rowSO-20335
ValueCountFrequency (%)
wb-2185037
 
0.4%
jl-1583534
 
0.3%
ma-1756034
 
0.3%
pp-1895534
 
0.3%
eh-1376532
 
0.3%
jd-1589532
 
0.3%
ck-1220532
 
0.3%
sv-2036532
 
0.3%
ep-1391531
 
0.3%
ap-1091531
 
0.3%
Other values (783)9665
96.7%
2025-11-23T23:58:49.247458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
111915
14.9%
-9994
12.5%
08532
 
10.7%
57865
 
9.8%
24682
 
5.9%
72931
 
3.7%
62909
 
3.6%
92904
 
3.6%
82818
 
3.5%
32779
 
3.5%
Other values (30)22623
28.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)79952
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
111915
14.9%
-9994
12.5%
08532
 
10.7%
57865
 
9.8%
24682
 
5.9%
72931
 
3.7%
62909
 
3.6%
92904
 
3.6%
82818
 
3.5%
32779
 
3.5%
Other values (30)22623
28.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)79952
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
111915
14.9%
-9994
12.5%
08532
 
10.7%
57865
 
9.8%
24682
 
5.9%
72931
 
3.7%
62909
 
3.6%
92904
 
3.6%
82818
 
3.5%
32779
 
3.5%
Other values (30)22623
28.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)79952
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
111915
14.9%
-9994
12.5%
08532
 
10.7%
57865
 
9.8%
24682
 
5.9%
72931
 
3.7%
62909
 
3.6%
92904
 
3.6%
82818
 
3.5%
32779
 
3.5%
Other values (30)22623
28.3%
Distinct793
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size607.6 KiB
2025-11-23T23:58:49.483175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length22
Median length18
Mean length12.960676
Min length7

Characters and Unicode

Total characters129529
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowClaire Gute
2nd rowClaire Gute
3rd rowDarrin Van Huff
4th rowSean O'Donnell
5th rowSean O'Donnell
ValueCountFrequency (%)
michael120
 
0.6%
frank112
 
0.6%
john107
 
0.5%
patrick96
 
0.5%
brian93
 
0.5%
stewart93
 
0.5%
paul92
 
0.5%
rick91
 
0.5%
ken91
 
0.5%
matt86
 
0.4%
Other values (901)19072
95.1%
2025-11-23T23:58:49.926309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a12011
 
9.3%
e11836
 
9.1%
n10241
 
7.9%
10059
 
7.8%
r9530
 
7.4%
i7919
 
6.1%
l6494
 
5.0%
o5850
 
4.5%
t5435
 
4.2%
s4546
 
3.5%
Other values (47)45608
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)129529
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a12011
 
9.3%
e11836
 
9.1%
n10241
 
7.9%
10059
 
7.8%
r9530
 
7.4%
i7919
 
6.1%
l6494
 
5.0%
o5850
 
4.5%
t5435
 
4.2%
s4546
 
3.5%
Other values (47)45608
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)129529
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a12011
 
9.3%
e11836
 
9.1%
n10241
 
7.9%
10059
 
7.8%
r9530
 
7.4%
i7919
 
6.1%
l6494
 
5.0%
o5850
 
4.5%
t5435
 
4.2%
s4546
 
3.5%
Other values (47)45608
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)129529
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a12011
 
9.3%
e11836
 
9.1%
n10241
 
7.9%
10059
 
7.8%
r9530
 
7.4%
i7919
 
6.1%
l6494
 
5.0%
o5850
 
4.5%
t5435
 
4.2%
s4546
 
3.5%
Other values (47)45608
35.2%

Segment
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size564.6 KiB
Consumer
5191 
Corporate
3020 
Home Office
1783 

Length

Max length11
Median length8
Mean length8.8374024
Min length8

Characters and Unicode

Total characters88321
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConsumer
2nd rowConsumer
3rd rowCorporate
4th rowConsumer
5th rowConsumer

Common Values

ValueCountFrequency (%)
Consumer5191
51.9%
Corporate3020
30.2%
Home Office1783
 
17.8%

Length

2025-11-23T23:58:50.042427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T23:58:50.115670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
consumer5191
44.1%
corporate3020
25.6%
home1783
 
15.1%
office1783
 
15.1%

Most occurring characters

ValueCountFrequency (%)
o13014
14.7%
e11777
13.3%
r11231
12.7%
C8211
9.3%
m6974
7.9%
u5191
 
5.9%
s5191
 
5.9%
n5191
 
5.9%
f3566
 
4.0%
p3020
 
3.4%
Other values (7)14955
16.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)88321
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o13014
14.7%
e11777
13.3%
r11231
12.7%
C8211
9.3%
m6974
7.9%
u5191
 
5.9%
s5191
 
5.9%
n5191
 
5.9%
f3566
 
4.0%
p3020
 
3.4%
Other values (7)14955
16.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)88321
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o13014
14.7%
e11777
13.3%
r11231
12.7%
C8211
9.3%
m6974
7.9%
u5191
 
5.9%
s5191
 
5.9%
n5191
 
5.9%
f3566
 
4.0%
p3020
 
3.4%
Other values (7)14955
16.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)88321
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o13014
14.7%
e11777
13.3%
r11231
12.7%
C8211
9.3%
m6974
7.9%
u5191
 
5.9%
s5191
 
5.9%
n5191
 
5.9%
f3566
 
4.0%
p3020
 
3.4%
Other values (7)14955
16.9%

Country
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size605.2 KiB
United States
9994 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters129922
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnited States
2nd rowUnited States
3rd rowUnited States
4th rowUnited States
5th rowUnited States

Common Values

ValueCountFrequency (%)
United States9994
100.0%

Length

2025-11-23T23:58:50.200265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T23:58:50.261376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
united9994
50.0%
states9994
50.0%

Most occurring characters

ValueCountFrequency (%)
t29982
23.1%
e19988
15.4%
n9994
 
7.7%
U9994
 
7.7%
i9994
 
7.7%
d9994
 
7.7%
9994
 
7.7%
S9994
 
7.7%
a9994
 
7.7%
s9994
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)129922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t29982
23.1%
e19988
15.4%
n9994
 
7.7%
U9994
 
7.7%
i9994
 
7.7%
d9994
 
7.7%
9994
 
7.7%
S9994
 
7.7%
a9994
 
7.7%
s9994
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)129922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t29982
23.1%
e19988
15.4%
n9994
 
7.7%
U9994
 
7.7%
i9994
 
7.7%
d9994
 
7.7%
9994
 
7.7%
S9994
 
7.7%
a9994
 
7.7%
s9994
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)129922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t29982
23.1%
e19988
15.4%
n9994
 
7.7%
U9994
 
7.7%
i9994
 
7.7%
d9994
 
7.7%
9994
 
7.7%
S9994
 
7.7%
a9994
 
7.7%
s9994
 
7.7%

City
Text

Distinct531
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size569.4 KiB
2025-11-23T23:58:50.529855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length17
Median length14
Mean length9.3306984
Min length4

Characters and Unicode

Total characters93251
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)0.7%

Sample

1st rowHenderson
2nd rowHenderson
3rd rowLos Angeles
4th rowFort Lauderdale
5th rowFort Lauderdale
ValueCountFrequency (%)
city994
 
7.0%
new937
 
6.6%
york920
 
6.5%
san805
 
5.7%
los747
 
5.2%
angeles747
 
5.2%
philadelphia537
 
3.8%
francisco510
 
3.6%
seattle428
 
3.0%
houston377
 
2.6%
Other values (555)7234
50.8%
2025-11-23T23:58:51.000140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e8719
 
9.4%
a7591
 
8.1%
o7499
 
8.0%
i6229
 
6.7%
n6199
 
6.6%
l5986
 
6.4%
s4699
 
5.0%
r4468
 
4.8%
t4438
 
4.8%
4242
 
4.5%
Other values (41)33181
35.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)93251
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e8719
 
9.4%
a7591
 
8.1%
o7499
 
8.0%
i6229
 
6.7%
n6199
 
6.6%
l5986
 
6.4%
s4699
 
5.0%
r4468
 
4.8%
t4438
 
4.8%
4242
 
4.5%
Other values (41)33181
35.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)93251
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e8719
 
9.4%
a7591
 
8.1%
o7499
 
8.0%
i6229
 
6.7%
n6199
 
6.6%
l5986
 
6.4%
s4699
 
5.0%
r4468
 
4.8%
t4438
 
4.8%
4242
 
4.5%
Other values (41)33181
35.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)93251
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e8719
 
9.4%
a7591
 
8.1%
o7499
 
8.0%
i6229
 
6.7%
n6199
 
6.6%
l5986
 
6.4%
s4699
 
5.0%
r4468
 
4.8%
t4438
 
4.8%
4242
 
4.5%
Other values (41)33181
35.6%

State
Categorical

High correlation 

Distinct49
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size561.2 KiB
California
2001 
New York
1128 
Texas
985 
Pennsylvania
587 
Washington
506 
Other values (44)
4787 

Length

Max length20
Median length14
Mean length8.4871923
Min length4

Characters and Unicode

Total characters84821
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowKentucky
2nd rowKentucky
3rd rowCalifornia
4th rowFlorida
5th rowFlorida

Common Values

ValueCountFrequency (%)
California2001
20.0%
New York1128
 
11.3%
Texas985
 
9.9%
Pennsylvania587
 
5.9%
Washington506
 
5.1%
Illinois492
 
4.9%
Ohio469
 
4.7%
Florida383
 
3.8%
Michigan255
 
2.6%
North Carolina249
 
2.5%
Other values (39)2939
29.4%

Length

2025-11-23T23:58:51.106854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
california2001
17.1%
new1322
 
11.3%
york1128
 
9.6%
texas985
 
8.4%
pennsylvania587
 
5.0%
washington506
 
4.3%
illinois492
 
4.2%
ohio469
 
4.0%
florida383
 
3.3%
carolina291
 
2.5%
Other values (43)3542
30.3%

Most occurring characters

ValueCountFrequency (%)
a10758
12.7%
i9895
11.7%
n8090
 
9.5%
o7323
 
8.6%
r5544
 
6.5%
e5051
 
6.0%
l4822
 
5.7%
s4604
 
5.4%
C2566
 
3.0%
f2011
 
2.4%
Other values (36)24157
28.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)84821
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a10758
12.7%
i9895
11.7%
n8090
 
9.5%
o7323
 
8.6%
r5544
 
6.5%
e5051
 
6.0%
l4822
 
5.7%
s4604
 
5.4%
C2566
 
3.0%
f2011
 
2.4%
Other values (36)24157
28.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)84821
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a10758
12.7%
i9895
11.7%
n8090
 
9.5%
o7323
 
8.6%
r5544
 
6.5%
e5051
 
6.0%
l4822
 
5.7%
s4604
 
5.4%
C2566
 
3.0%
f2011
 
2.4%
Other values (36)24157
28.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)84821
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a10758
12.7%
i9895
11.7%
n8090
 
9.5%
o7323
 
8.6%
r5544
 
6.5%
e5051
 
6.0%
l4822
 
5.7%
s4604
 
5.4%
C2566
 
3.0%
f2011
 
2.4%
Other values (36)24157
28.5%

Postal Code
Real number (ℝ)

High correlation 

Distinct631
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55190.379
Minimum1040
Maximum99301
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-11-23T23:58:51.204720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1040
5-th percentile10009
Q123223
median56430.5
Q390008
95-th percentile98006
Maximum99301
Range98261
Interquartile range (IQR)66785

Descriptive statistics

Standard deviation32063.693
Coefficient of variation (CV)0.58096526
Kurtosis-1.4930202
Mean55190.379
Median Absolute Deviation (MAD)33573.5
Skewness-0.12852552
Sum5.5157265 × 108
Variance1.0280804 × 109
MonotonicityNot monotonic
2025-11-23T23:58:51.308837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10035263
 
2.6%
10024230
 
2.3%
10009229
 
2.3%
94122203
 
2.0%
10011193
 
1.9%
94110166
 
1.7%
98105165
 
1.7%
19134160
 
1.6%
90049151
 
1.5%
98103151
 
1.5%
Other values (621)8083
80.9%
ValueCountFrequency (%)
10401
 
< 0.1%
14536
 
0.1%
17522
 
< 0.1%
18104
 
< 0.1%
184133
0.3%
185216
0.2%
19153
 
< 0.1%
203817
0.2%
21386
 
0.1%
21483
 
< 0.1%
ValueCountFrequency (%)
993016
 
0.1%
992077
 
0.1%
986615
 
0.1%
986323
 
< 0.1%
985025
 
0.1%
982702
 
< 0.1%
982263
 
< 0.1%
982081
 
< 0.1%
981987
 
0.1%
98115112
1.1%

Region
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size525.8 KiB
West
3203 
East
2848 
Central
2323 
South
1620 

Length

Max length7
Median length4
Mean length4.8594156
Min length4

Characters and Unicode

Total characters48565
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth
2nd rowSouth
3rd rowWest
4th rowSouth
5th rowSouth

Common Values

ValueCountFrequency (%)
West3203
32.0%
East2848
28.5%
Central2323
23.2%
South1620
16.2%

Length

2025-11-23T23:58:51.402770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T23:58:51.467056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
west3203
32.0%
east2848
28.5%
central2323
23.2%
south1620
16.2%

Most occurring characters

ValueCountFrequency (%)
t9994
20.6%
s6051
12.5%
e5526
11.4%
a5171
10.6%
W3203
 
6.6%
E2848
 
5.9%
C2323
 
4.8%
n2323
 
4.8%
r2323
 
4.8%
l2323
 
4.8%
Other values (4)6480
13.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)48565
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t9994
20.6%
s6051
12.5%
e5526
11.4%
a5171
10.6%
W3203
 
6.6%
E2848
 
5.9%
C2323
 
4.8%
n2323
 
4.8%
r2323
 
4.8%
l2323
 
4.8%
Other values (4)6480
13.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)48565
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t9994
20.6%
s6051
12.5%
e5526
11.4%
a5171
10.6%
W3203
 
6.6%
E2848
 
5.9%
C2323
 
4.8%
n2323
 
4.8%
r2323
 
4.8%
l2323
 
4.8%
Other values (4)6480
13.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)48565
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t9994
20.6%
s6051
12.5%
e5526
11.4%
a5171
10.6%
W3203
 
6.6%
E2848
 
5.9%
C2323
 
4.8%
n2323
 
4.8%
r2323
 
4.8%
l2323
 
4.8%
Other values (4)6480
13.3%
Distinct1862
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Memory size624.8 KiB
2025-11-23T23:58:51.629165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters149910
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)0.9%

Sample

1st rowFUR-BO-10001798
2nd rowFUR-CH-10000454
3rd rowOFF-LA-10000240
4th rowFUR-TA-10000577
5th rowOFF-ST-10000760
ValueCountFrequency (%)
off-pa-1000197019
 
0.2%
tec-ac-1000383218
 
0.2%
fur-fu-1000427016
 
0.2%
fur-ch-1000114615
 
0.2%
tec-ac-1000204915
 
0.2%
tec-ac-1000362815
 
0.2%
fur-ch-1000264715
 
0.2%
fur-fu-1000147314
 
0.1%
off-bi-1000152414
 
0.1%
fur-ch-1000288014
 
0.1%
Other values (1852)9839
98.4%
2025-11-23T23:58:51.881197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
035052
23.4%
-19988
13.3%
F15347
10.2%
114995
10.0%
O6322
 
4.2%
24862
 
3.2%
44831
 
3.2%
34805
 
3.2%
A4422
 
2.9%
53401
 
2.3%
Other values (17)35885
23.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)149910
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
035052
23.4%
-19988
13.3%
F15347
10.2%
114995
10.0%
O6322
 
4.2%
24862
 
3.2%
44831
 
3.2%
34805
 
3.2%
A4422
 
2.9%
53401
 
2.3%
Other values (17)35885
23.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)149910
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
035052
23.4%
-19988
13.3%
F15347
10.2%
114995
10.0%
O6322
 
4.2%
24862
 
3.2%
44831
 
3.2%
34805
 
3.2%
A4422
 
2.9%
53401
 
2.3%
Other values (17)35885
23.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)149910
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
035052
23.4%
-19988
13.3%
F15347
10.2%
114995
10.0%
O6322
 
4.2%
24862
 
3.2%
44831
 
3.2%
34805
 
3.2%
A4422
 
2.9%
53401
 
2.3%
Other values (17)35885
23.9%

Category
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size603.3 KiB
Office Supplies
6026 
Furniture
2121 
Technology
1847 

Length

Max length15
Median length15
Mean length12.802582
Min length9

Characters and Unicode

Total characters127949
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFurniture
2nd rowFurniture
3rd rowOffice Supplies
4th rowFurniture
5th rowOffice Supplies

Common Values

ValueCountFrequency (%)
Office Supplies6026
60.3%
Furniture2121
 
21.2%
Technology1847
 
18.5%

Length

2025-11-23T23:58:51.973282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T23:58:52.032529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
office6026
37.6%
supplies6026
37.6%
furniture2121
 
13.2%
technology1847
 
11.5%

Most occurring characters

ValueCountFrequency (%)
e16020
12.5%
i14173
11.1%
p12052
9.4%
f12052
9.4%
u10268
 
8.0%
c7873
 
6.2%
l7873
 
6.2%
O6026
 
4.7%
S6026
 
4.7%
6026
 
4.7%
Other values (10)29560
23.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)127949
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e16020
12.5%
i14173
11.1%
p12052
9.4%
f12052
9.4%
u10268
 
8.0%
c7873
 
6.2%
l7873
 
6.2%
O6026
 
4.7%
S6026
 
4.7%
6026
 
4.7%
Other values (10)29560
23.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)127949
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e16020
12.5%
i14173
11.1%
p12052
9.4%
f12052
9.4%
u10268
 
8.0%
c7873
 
6.2%
l7873
 
6.2%
O6026
 
4.7%
S6026
 
4.7%
6026
 
4.7%
Other values (10)29560
23.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)127949
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e16020
12.5%
i14173
11.1%
p12052
9.4%
f12052
9.4%
u10268
 
8.0%
c7873
 
6.2%
l7873
 
6.2%
O6026
 
4.7%
S6026
 
4.7%
6026
 
4.7%
Other values (10)29560
23.1%

Sub-Category
Categorical

High correlation 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size548.5 KiB
Binders
1523 
Paper
1370 
Furnishings
957 
Phones
889 
Storage
846 
Other values (12)
4409 

Length

Max length11
Median length9
Mean length7.191715
Min length3

Characters and Unicode

Total characters71874
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBookcases
2nd rowChairs
3rd rowLabels
4th rowTables
5th rowStorage

Common Values

ValueCountFrequency (%)
Binders1523
15.2%
Paper1370
13.7%
Furnishings957
9.6%
Phones889
8.9%
Storage846
8.5%
Art796
8.0%
Accessories775
7.8%
Chairs617
6.2%
Appliances466
 
4.7%
Labels364
 
3.6%
Other values (7)1391
13.9%

Length

2025-11-23T23:58:52.111421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
binders1523
15.2%
paper1370
13.7%
furnishings957
9.6%
phones889
8.9%
storage846
8.5%
art796
8.0%
accessories775
7.8%
chairs617
6.2%
appliances466
 
4.7%
labels364
 
3.6%
Other values (7)1391
13.9%

Most occurring characters

ValueCountFrequency (%)
s9934
13.8%
e8870
12.3%
r7169
 
10.0%
i5668
 
7.9%
n5378
 
7.5%
a4542
 
6.3%
o3288
 
4.6%
p3004
 
4.2%
h2578
 
3.6%
c2359
 
3.3%
Other values (18)19084
26.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)71874
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s9934
13.8%
e8870
12.3%
r7169
 
10.0%
i5668
 
7.9%
n5378
 
7.5%
a4542
 
6.3%
o3288
 
4.6%
p3004
 
4.2%
h2578
 
3.6%
c2359
 
3.3%
Other values (18)19084
26.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)71874
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s9934
13.8%
e8870
12.3%
r7169
 
10.0%
i5668
 
7.9%
n5378
 
7.5%
a4542
 
6.3%
o3288
 
4.6%
p3004
 
4.2%
h2578
 
3.6%
c2359
 
3.3%
Other values (18)19084
26.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)71874
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s9934
13.8%
e8870
12.3%
r7169
 
10.0%
i5668
 
7.9%
n5378
 
7.5%
a4542
 
6.3%
o3288
 
4.6%
p3004
 
4.2%
h2578
 
3.6%
c2359
 
3.3%
Other values (18)19084
26.6%
Distinct1850
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Memory size856.6 KiB
2025-11-23T23:58:52.354990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length127
Median length78
Mean length36.91605
Min length5

Characters and Unicode

Total characters368939
Distinct characters85
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)0.9%

Sample

1st rowBush Somerset Collection Bookcase
2nd rowHon Deluxe Fabric Upholstered Stacking Chairs, Rounded Back
3rd rowSelf-Adhesive Address Labels for Typewriters by Universal
4th rowBretford CR4500 Series Slim Rectangular Table
5th rowEldon Fold 'N Roll Cart System
ValueCountFrequency (%)
xerox865
 
1.5%
x701
 
1.3%
with599
 
1.1%
599
 
1.1%
avery557
 
1.0%
for539
 
1.0%
binders524
 
0.9%
chair479
 
0.9%
black426
 
0.8%
phone374
 
0.7%
Other values (2798)50371
89.9%
2025-11-23T23:58:52.720028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
45670
 
12.4%
e33538
 
9.1%
r20791
 
5.6%
o19902
 
5.4%
a19064
 
5.2%
i18648
 
5.1%
l16365
 
4.4%
n15622
 
4.2%
s14683
 
4.0%
t14550
 
3.9%
Other values (75)150106
40.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)368939
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
45670
 
12.4%
e33538
 
9.1%
r20791
 
5.6%
o19902
 
5.4%
a19064
 
5.2%
i18648
 
5.1%
l16365
 
4.4%
n15622
 
4.2%
s14683
 
4.0%
t14550
 
3.9%
Other values (75)150106
40.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)368939
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
45670
 
12.4%
e33538
 
9.1%
r20791
 
5.6%
o19902
 
5.4%
a19064
 
5.2%
i18648
 
5.1%
l16365
 
4.4%
n15622
 
4.2%
s14683
 
4.0%
t14550
 
3.9%
Other values (75)150106
40.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)368939
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
45670
 
12.4%
e33538
 
9.1%
r20791
 
5.6%
o19902
 
5.4%
a19064
 
5.2%
i18648
 
5.1%
l16365
 
4.4%
n15622
 
4.2%
s14683
 
4.0%
t14550
 
3.9%
Other values (75)150106
40.7%

Sales
Real number (ℝ)

High correlation 

Distinct5825
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean229.858
Minimum0.444
Maximum22638.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-11-23T23:58:52.826094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.444
5-th percentile4.98
Q117.28
median54.49
Q3209.94
95-th percentile956.98425
Maximum22638.48
Range22638.036
Interquartile range (IQR)192.66

Descriptive statistics

Standard deviation623.2451
Coefficient of variation (CV)2.7114353
Kurtosis305.31175
Mean229.858
Median Absolute Deviation (MAD)45.406
Skewness12.972752
Sum2297200.9
Variance388434.46
MonotonicityNot monotonic
2025-11-23T23:58:52.929511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.9656
 
0.6%
19.4439
 
0.4%
15.55239
 
0.4%
10.36836
 
0.4%
25.9236
 
0.4%
32.428
 
0.3%
6.4821
 
0.2%
17.9421
 
0.2%
20.73619
 
0.2%
14.9417
 
0.2%
Other values (5815)9682
96.9%
ValueCountFrequency (%)
0.4441
 
< 0.1%
0.5561
 
< 0.1%
0.8361
 
< 0.1%
0.8521
 
< 0.1%
0.8761
 
< 0.1%
0.8981
 
< 0.1%
0.9841
 
< 0.1%
0.991
 
< 0.1%
1.0441
 
< 0.1%
1.083
< 0.1%
ValueCountFrequency (%)
22638.481
< 0.1%
17499.951
< 0.1%
13999.961
< 0.1%
11199.9681
< 0.1%
10499.971
< 0.1%
9892.741
< 0.1%
9449.951
< 0.1%
9099.931
< 0.1%
8749.951
< 0.1%
8399.9761
< 0.1%

Quantity
Real number (ℝ)

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7895737
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-11-23T23:58:53.005020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile8
Maximum14
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2251097
Coefficient of variation (CV)0.58716622
Kurtosis1.9918894
Mean3.7895737
Median Absolute Deviation (MAD)1
Skewness1.2785448
Sum37873
Variance4.9511131
MonotonicityNot monotonic
2025-11-23T23:58:53.076843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
32409
24.1%
22402
24.0%
51230
12.3%
41191
11.9%
1899
 
9.0%
7606
 
6.1%
6572
 
5.7%
9258
 
2.6%
8257
 
2.6%
1057
 
0.6%
Other values (4)113
 
1.1%
ValueCountFrequency (%)
1899
 
9.0%
22402
24.0%
32409
24.1%
41191
11.9%
51230
12.3%
6572
 
5.7%
7606
 
6.1%
8257
 
2.6%
9258
 
2.6%
1057
 
0.6%
ValueCountFrequency (%)
1429
 
0.3%
1327
 
0.3%
1223
 
0.2%
1134
 
0.3%
1057
 
0.6%
9258
 
2.6%
8257
 
2.6%
7606
6.1%
6572
5.7%
51230
12.3%

Discount
Real number (ℝ)

High correlation  Zeros 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15620272
Minimum0
Maximum0.8
Zeros4798
Zeros (%)48.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-11-23T23:58:53.142446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.2
Q30.2
95-th percentile0.7
Maximum0.8
Range0.8
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.20645197
Coefficient of variation (CV)1.3216925
Kurtosis2.4095461
Mean0.15620272
Median Absolute Deviation (MAD)0.2
Skewness1.6842947
Sum1561.09
Variance0.042622415
MonotonicityNot monotonic
2025-11-23T23:58:53.214954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
04798
48.0%
0.23657
36.6%
0.7418
 
4.2%
0.8300
 
3.0%
0.3227
 
2.3%
0.4206
 
2.1%
0.6138
 
1.4%
0.194
 
0.9%
0.566
 
0.7%
0.1552
 
0.5%
Other values (2)38
 
0.4%
ValueCountFrequency (%)
04798
48.0%
0.194
 
0.9%
0.1552
 
0.5%
0.23657
36.6%
0.3227
 
2.3%
0.3227
 
0.3%
0.4206
 
2.1%
0.4511
 
0.1%
0.566
 
0.7%
0.6138
 
1.4%
ValueCountFrequency (%)
0.8300
 
3.0%
0.7418
 
4.2%
0.6138
 
1.4%
0.566
 
0.7%
0.4511
 
0.1%
0.4206
 
2.1%
0.3227
 
0.3%
0.3227
 
2.3%
0.23657
36.6%
0.1552
 
0.5%

Profit
Real number (ℝ)

High correlation 

Distinct7287
Distinct (%)72.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.656896
Minimum-6599.978
Maximum8399.976
Zeros65
Zeros (%)0.7%
Negative1871
Negative (%)18.7%
Memory size78.2 KiB
2025-11-23T23:58:53.301838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-6599.978
5-th percentile-53.03092
Q11.72875
median8.6665
Q329.364
95-th percentile168.4704
Maximum8399.976
Range14999.954
Interquartile range (IQR)27.63525

Descriptive statistics

Standard deviation234.26011
Coefficient of variation (CV)8.1746504
Kurtosis397.18851
Mean28.656896
Median Absolute Deviation (MAD)10.77855
Skewness7.5614316
Sum286397.02
Variance54877.798
MonotonicityNot monotonic
2025-11-23T23:58:53.407276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
065
 
0.7%
6.220843
 
0.4%
9.331238
 
0.4%
3.628832
 
0.3%
5.443232
 
0.3%
15.55226
 
0.3%
12.441621
 
0.2%
7.257619
 
0.2%
3.110418
 
0.2%
9.07211
 
0.1%
Other values (7277)9689
96.9%
ValueCountFrequency (%)
-6599.9781
< 0.1%
-3839.99041
< 0.1%
-3701.89281
< 0.1%
-3399.981
< 0.1%
-2929.48451
< 0.1%
-2639.99121
< 0.1%
-2287.7821
< 0.1%
-1862.31241
< 0.1%
-1850.94641
< 0.1%
-1811.07841
< 0.1%
ValueCountFrequency (%)
8399.9761
< 0.1%
6719.98081
< 0.1%
5039.98561
< 0.1%
4946.371
< 0.1%
4630.47551
< 0.1%
3919.98881
< 0.1%
3177.4751
< 0.1%
2799.9841
< 0.1%
2591.95681
< 0.1%
2504.22161
< 0.1%

Cost
Real number (ℝ)

High correlation 

Distinct6632
Distinct (%)66.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201.2011
Minimum0.5544
Maximum24449.558
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-11-23T23:58:53.517625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.5544
5-th percentile4.034535
Q112.6882
median41.664
Q3182.2263
95-th percentile863.04572
Maximum24449.558
Range24449.004
Interquartile range (IQR)169.5381

Descriptive statistics

Standard deviation550.83941
Coefficient of variation (CV)2.7377554
Kurtosis454.59221
Mean201.2011
Median Absolute Deviation (MAD)35.086
Skewness14.753072
Sum2010803.8
Variance303424.06
MonotonicityNot monotonic
2025-11-23T23:58:53.626004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.739243
 
0.4%
10.108838
 
0.4%
6.739232
 
0.3%
10.108832
 
0.3%
16.84826
 
0.3%
13.478421
 
0.2%
13.478419
 
0.2%
3.369618
 
0.2%
7.918212
 
0.1%
2.937611
 
0.1%
Other values (6622)9742
97.5%
ValueCountFrequency (%)
0.55441
< 0.1%
0.65721
< 0.1%
0.841
< 0.1%
0.842
< 0.1%
0.87361
< 0.1%
0.9021
< 0.1%
0.9361
< 0.1%
1.08122
< 0.1%
1.0892
< 0.1%
1.10881
< 0.1%
ValueCountFrequency (%)
24449.55841
< 0.1%
11839.97041
< 0.1%
11099.9631
< 0.1%
9519.9441
< 0.1%
9099.9741
< 0.1%
7860.1441
< 0.1%
7279.97922
< 0.1%
7279.97921
< 0.1%
6733.94821
< 0.1%
6159.95641
< 0.1%

profit_margin
Real number (ℝ)

High correlation 

Distinct525
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.031393
Minimum-275
Maximum50
Zeros65
Zeros (%)0.7%
Negative1871
Negative (%)18.7%
Memory size78.2 KiB
2025-11-23T23:58:53.723837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-275
5-th percentile-76.666667
Q17.5
median27
Q336.25
95-th percentile48
Maximum50
Range325
Interquartile range (IQR)28.75

Descriptive statistics

Standard deviation46.675435
Coefficient of variation (CV)3.8794705
Kurtosis10.173344
Mean12.031393
Median Absolute Deviation (MAD)17
Skewness-2.8948263
Sum120241.74
Variance2178.5962
MonotonicityNot monotonic
2025-11-23T23:58:53.849232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35431
 
4.3%
47324
 
3.2%
33.75285
 
2.9%
26258
 
2.6%
46251
 
2.5%
10228
 
2.3%
48204
 
2.0%
48199
 
2.0%
8.75178
 
1.8%
36.25177
 
1.8%
Other values (515)7459
74.6%
ValueCountFrequency (%)
-2754
 
< 0.1%
-27014
0.1%
-2655
 
0.1%
-2606
0.1%
-2604
 
< 0.1%
-2554
 
< 0.1%
-2559
0.1%
-2509
0.1%
-2501
 
< 0.1%
-2451
 
< 0.1%
ValueCountFrequency (%)
50140
1.4%
4979
 
0.8%
49157
1.6%
4979
 
0.8%
48199
2.0%
48204
2.0%
48106
 
1.1%
4717
 
0.2%
47324
3.2%
4712
 
0.1%

is_gain
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size488.1 KiB
1
8058 
0
1936 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9994
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
18058
80.6%
01936
 
19.4%

Length

2025-11-23T23:58:53.940968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T23:58:53.993159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
18058
80.6%
01936
 
19.4%

Most occurring characters

ValueCountFrequency (%)
18058
80.6%
01936
 
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)9994
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
18058
80.6%
01936
 
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9994
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
18058
80.6%
01936
 
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9994
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
18058
80.6%
01936
 
19.4%

Days_to_ship
Real number (ℝ)

High correlation  Zeros 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9581749
Minimum0
Maximum7
Zeros519
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-11-23T23:58:54.036680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median4
Q35
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7475667
Coefficient of variation (CV)0.44150822
Kurtosis-0.28755198
Mean3.9581749
Median Absolute Deviation (MAD)1
Skewness-0.42132235
Sum39558
Variance3.0539895
MonotonicityNot monotonic
2025-11-23T23:58:54.102984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
42774
27.8%
52169
21.7%
21334
13.3%
61203
12.0%
31005
 
10.1%
7621
 
6.2%
0519
 
5.2%
1369
 
3.7%
ValueCountFrequency (%)
0519
 
5.2%
1369
 
3.7%
21334
13.3%
31005
 
10.1%
42774
27.8%
52169
21.7%
61203
12.0%
7621
 
6.2%
ValueCountFrequency (%)
7621
 
6.2%
61203
12.0%
52169
21.7%
42774
27.8%
31005
 
10.1%
21334
13.3%
1369
 
3.7%
0519
 
5.2%

sales_per_quantity
Real number (ℝ)

High correlation 

Distinct3298
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.919569
Minimum0.336
Maximum3773.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-11-23T23:58:54.184692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.336
5-th percentile1.93155
Q15.47
median16.27
Q363.94
95-th percentile243.9842
Maximum3773.08
Range3772.744
Interquartile range (IQR)58.47

Descriptive statistics

Standard deviation142.92744
Coefficient of variation (CV)2.3461663
Kurtosis197.7996
Mean60.919569
Median Absolute Deviation (MAD)13.318
Skewness10.782635
Sum608830.17
Variance20428.253
MonotonicityNot monotonic
2025-11-23T23:58:54.281986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.48158
 
1.6%
5.184131
 
1.3%
5.9849
 
0.5%
6.4838
 
0.4%
2.8837
 
0.4%
3.98433
 
0.3%
4.1332
 
0.3%
6.6832
 
0.3%
30.9830
 
0.3%
29.9930
 
0.3%
Other values (3288)9424
94.3%
ValueCountFrequency (%)
0.3361
< 0.1%
0.3361
< 0.1%
0.361
< 0.1%
0.362
< 0.1%
0.3961
< 0.1%
0.3961
< 0.1%
0.4161
< 0.1%
0.4161
< 0.1%
0.4321
< 0.1%
0.4441
< 0.1%
ValueCountFrequency (%)
3773.081
< 0.1%
3499.991
< 0.1%
3499.992
< 0.1%
2799.9921
< 0.1%
2399.9921
< 0.1%
2099.9941
< 0.1%
1999.9951
< 0.1%
1995.991
< 0.1%
1889.991
< 0.1%
1749.991
< 0.1%

month_order
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8096858
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-11-23T23:58:54.367221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median9
Q311
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.2846544
Coefficient of variation (CV)0.42058727
Kurtosis-0.99132786
Mean7.8096858
Median Absolute Deviation (MAD)2
Skewness-0.42969297
Sum78050
Variance10.788955
MonotonicityNot monotonic
2025-11-23T23:58:54.438391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
111471
14.7%
121408
14.1%
91383
13.8%
10819
8.2%
5735
7.4%
6717
7.2%
7710
7.1%
8706
7.1%
3696
7.0%
4668
6.7%
Other values (2)681
6.8%
ValueCountFrequency (%)
1381
 
3.8%
2300
 
3.0%
3696
7.0%
4668
6.7%
5735
7.4%
6717
7.2%
7710
7.1%
8706
7.1%
91383
13.8%
10819
8.2%
ValueCountFrequency (%)
121408
14.1%
111471
14.7%
10819
8.2%
91383
13.8%
8706
7.1%
7710
7.1%
6717
7.2%
5735
7.4%
4668
6.7%
3696
7.0%

year_order
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size517.4 KiB
2017
3312 
2016
2587 
2015
2102 
2014
1993 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters39976
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
20173312
33.1%
20162587
25.9%
20152102
21.0%
20141993
19.9%

Length

2025-11-23T23:58:54.521148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T23:58:54.588351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
20173312
33.1%
20162587
25.9%
20152102
21.0%
20141993
19.9%

Most occurring characters

ValueCountFrequency (%)
29994
25.0%
09994
25.0%
19994
25.0%
73312
 
8.3%
62587
 
6.5%
52102
 
5.3%
41993
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)39976
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
29994
25.0%
09994
25.0%
19994
25.0%
73312
 
8.3%
62587
 
6.5%
52102
 
5.3%
41993
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)39976
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
29994
25.0%
09994
25.0%
19994
25.0%
73312
 
8.3%
62587
 
6.5%
52102
 
5.3%
41993
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)39976
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
29994
25.0%
09994
25.0%
19994
25.0%
73312
 
8.3%
62587
 
6.5%
52102
 
5.3%
41993
 
5.0%

Interactions

2025-11-23T23:58:46.119105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:30.936042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:31.855294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:32.785386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:33.687364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:34.643104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:35.705119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:36.735935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:37.743073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:44.209261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:45.141070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:46.201225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:31.015766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:31.933447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:32.867893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:33.767448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:34.736368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:35.792057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:36.824259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:37.841918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:44.291559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-23T23:58:46.288328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:31.095950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:32.018689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:32.946976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:33.848408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:34.831993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:35.888199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-23T23:58:31.175891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:32.089431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:33.034990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:33.922753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:34.929152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:35.976754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:37.003124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:38.083043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:44.452957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:45.388446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:46.450485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:31.260687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:32.164520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:33.115357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:34.008451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:35.018973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:36.067841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:37.090811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:38.226924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:44.542849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:45.469951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:46.554747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:31.350528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:32.259912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:33.198579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:34.103329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:35.139901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:36.175158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-23T23:58:46.639783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:31.435367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-23T23:58:31.521541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:32.425894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-23T23:58:31.612753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:32.509109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:33.443500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:34.370294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-23T23:58:37.466340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:38.703507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:44.879737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:45.848364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-23T23:58:31.697467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:32.604046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:33.525457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:34.462959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:35.518015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:36.547708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:37.559894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:44.041840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:44.971577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:45.940047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:46.954071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:31.774670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:32.695023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:33.610742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:34.559395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:35.614040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:36.642960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:37.655809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:44.126524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:45.060476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T23:58:46.032570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-23T23:58:54.665537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
CategoryCostDays_to_shipDiscountPostal CodeProfitQuantityRegionRow IDSalesSegmentShip ModeStateSub-Categoryis_gainmonth_orderprofit_marginsales_per_quantityyear_order
Category1.0000.0450.0000.3770.0000.0560.0000.0000.0080.0720.0000.0000.0190.9990.2080.0140.2710.0990.000
Cost0.0451.000-0.0140.0940.0040.3830.3210.0050.0040.9750.0000.0000.0000.1280.0480.014-0.3860.9110.000
Days_to_ship0.000-0.0141.000-0.014-0.007-0.0070.0160.041-0.004-0.0150.0430.7810.1020.0010.0330.0040.004-0.0240.047
Discount0.3770.094-0.0141.0000.053-0.543-0.0010.2940.013-0.0570.0050.0270.3540.3530.814-0.001-0.645-0.0650.000
Postal Code0.0000.004-0.0070.0531.000-0.0050.0140.9210.011-0.0020.0350.0380.9680.0000.3580.031-0.028-0.0070.034
Profit0.0560.383-0.007-0.543-0.0051.0000.2340.021-0.0110.5180.0000.0050.0170.1300.1320.0170.5000.4580.000
Quantity0.0000.3210.016-0.0010.0140.2341.0000.000-0.0020.3270.0120.0000.0040.0000.0100.0220.001-0.0100.013
Region0.0000.0050.0410.2940.9210.0210.0001.0000.0380.0000.0000.0220.9980.0000.2030.0400.2030.0000.016
Row ID0.0080.004-0.0040.0130.011-0.011-0.0020.0381.000-0.0010.0300.0500.1020.0000.050-0.018-0.017-0.0010.040
Sales0.0720.975-0.015-0.057-0.0020.5180.3270.000-0.0011.0000.0020.0000.0000.1420.0110.015-0.2000.9340.000
Segment0.0000.0000.0430.0050.0350.0000.0120.0000.0300.0021.0000.0330.0900.0000.0100.0420.0170.0000.028
Ship Mode0.0000.0000.7810.0270.0380.0050.0000.0220.0500.0000.0331.0000.0960.0070.0380.0440.0120.0190.023
State0.0190.0000.1020.3540.9680.0170.0040.9980.1020.0000.0900.0961.0000.0000.5080.0980.2280.0260.089
Sub-Category0.9990.1280.0010.3530.0000.1300.0000.0000.0000.1420.0000.0070.0001.0000.4380.0000.3040.1860.000
is_gain0.2080.0480.0330.8140.3580.1320.0100.2030.0500.0110.0100.0380.5080.4381.0000.0000.8670.0000.000
month_order0.0140.0140.004-0.0010.0310.0170.0220.040-0.0180.0150.0420.0440.0980.0000.0001.0000.0020.0080.026
profit_margin0.271-0.3860.004-0.645-0.0280.5000.0010.203-0.017-0.2000.0170.0120.2280.3040.8670.0021.000-0.2110.000
sales_per_quantity0.0990.911-0.024-0.065-0.0070.458-0.0100.000-0.0010.9340.0000.0190.0260.1860.0000.008-0.2111.0000.009
year_order0.0000.0000.0470.0000.0340.0000.0130.0160.0400.0000.0280.0230.0890.0000.0000.0260.0000.0091.000

Missing values

2025-11-23T23:58:47.103165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-23T23:58:47.295357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Row IDOrder IDOrder DateShip DateShip ModeCustomer IDCustomer NameSegmentCountryCityStatePostal CodeRegionProduct IDCategorySub-CategoryProduct NameSalesQuantityDiscountProfitCostprofit_marginis_gainDays_to_shipsales_per_quantitymonth_orderyear_order
01CA-2016-1521562016-11-082016-11-11Second ClassCG-12520Claire GuteConsumerUnited StatesHendersonKentucky42420SouthFUR-BO-10001798FurnitureBookcasesBush Somerset Collection Bookcase261.960020.0041.9136220.046416.0013130.9800112016
12CA-2016-1521562016-11-082016-11-11Second ClassCG-12520Claire GuteConsumerUnited StatesHendersonKentucky42420SouthFUR-CH-10000454FurnitureChairsHon Deluxe Fabric Upholstered Stacking Chairs, Rounded Back731.940030.00219.5820512.358030.0013243.9800112016
23CA-2016-1386882016-06-122016-06-16Second ClassDV-13045Darrin Van HuffCorporateUnited StatesLos AngelesCalifornia90036WestOFF-LA-10000240Office SuppliesLabelsSelf-Adhesive Address Labels for Typewriters by Universal14.620020.006.87147.748647.00147.310062016
34US-2015-1089662015-10-112015-10-18Standard ClassSO-20335Sean O'DonnellConsumerUnited StatesFort LauderdaleFlorida33311SouthFUR-TA-10000577FurnitureTablesBretford CR4500 Series Slim Rectangular Table957.577550.45-383.03101340.6085-40.0007191.5155102015
45US-2015-1089662015-10-112015-10-18Standard ClassSO-20335Sean O'DonnellConsumerUnited StatesFort LauderdaleFlorida33311SouthOFF-ST-10000760Office SuppliesStorageEldon Fold 'N Roll Cart System22.368020.202.516419.851611.251711.1840102015
56CA-2014-1158122014-06-092014-06-14Standard ClassBH-11710Brosina HoffmanConsumerUnited StatesLos AngelesCalifornia90032WestFUR-FU-10001487FurnitureFurnishingsEldon Expressions Wood and Plastic Desk Accessories, Cherry Wood48.860070.0014.169434.690629.00156.980062014
67CA-2014-1158122014-06-092014-06-14Standard ClassBH-11710Brosina HoffmanConsumerUnited StatesLos AngelesCalifornia90032WestOFF-AR-10002833Office SuppliesArtNewell 3227.280040.001.96565.314427.00151.820062014
78CA-2014-1158122014-06-092014-06-14Standard ClassBH-11710Brosina HoffmanConsumerUnited StatesLos AngelesCalifornia90032WestTEC-PH-10002275TechnologyPhonesMitel 5320 IP Phone VoIP phone907.152060.2090.7152816.436810.0015151.192062014
89CA-2014-1158122014-06-092014-06-14Standard ClassBH-11710Brosina HoffmanConsumerUnited StatesLos AngelesCalifornia90032WestOFF-BI-10003910Office SuppliesBindersDXL Angle-View Binders with Locking Rings by Samsill18.504030.205.782512.721531.25156.168062014
910CA-2014-1158122014-06-092014-06-14Standard ClassBH-11710Brosina HoffmanConsumerUnited StatesLos AngelesCalifornia90032WestOFF-AP-10002892Office SuppliesAppliancesBelkin F5C206VTEL 6 Outlet Surge114.900050.0034.470080.430030.001522.980062014
Row IDOrder IDOrder DateShip DateShip ModeCustomer IDCustomer NameSegmentCountryCityStatePostal CodeRegionProduct IDCategorySub-CategoryProduct NameSalesQuantityDiscountProfitCostprofit_marginis_gainDays_to_shipsales_per_quantitymonth_orderyear_order
99849985CA-2015-1002512015-05-172015-05-23Standard ClassDV-13465Dianna VittoriniConsumerUnited StatesLong BeachNew York11561EastOFF-LA-10003766Office SuppliesLabelsSelf-Adhesive Removable Labels31.500100.015.120016.380048.00163.15052015
99859986CA-2015-1002512015-05-172015-05-23Standard ClassDV-13465Dianna VittoriniConsumerUnited StatesLong BeachNew York11561EastOFF-SU-10000898Office SuppliesSuppliesAcme Hot Forged Carbon Steel Scissors with Nickel-Plated Handles, 3 7/8" Cut, 8"L55.60040.016.124039.476029.001613.90052015
99869987CA-2016-1257942016-09-292016-10-03Standard ClassML-17410Maris LaWareConsumerUnited StatesLos AngelesCalifornia90008WestTEC-AC-10003399TechnologyAccessoriesMemorex Mini Travel Drive 64 GB USB 2.0 Flash Drive36.24010.015.220821.019242.001436.24092016
99879988CA-2017-1636292017-11-172017-11-21Standard ClassRA-19885Ruben AusmanCorporateUnited StatesAthensGeorgia30605SouthTEC-AC-10001539TechnologyAccessoriesLogitech G430 Surround Sound Gaming Headset with Dolby 7.1 Technology79.99010.028.796451.193636.001479.990112017
99889989CA-2017-1636292017-11-172017-11-21Standard ClassRA-19885Ruben AusmanCorporateUnited StatesAthensGeorgia30605SouthTEC-PH-10004006TechnologyPhonesPanasonic KX - TS880B Telephone206.10050.055.6470150.453027.001441.220112017
99899990CA-2014-1104222014-01-212014-01-23Second ClassTB-21400Tom BoeckenhauerConsumerUnited StatesMiamiFlorida33180SouthFUR-FU-10001889FurnitureFurnishingsUltra Door Pull Handle25.24830.24.102821.145216.25128.41612014
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